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Sparse Gaussian process for spatial function estimation with mobile sensor networks

Lu, B and Gu, D and Hu, H and McDonald-Maier, K (2012) Sparse Gaussian process for spatial function estimation with mobile sensor networks. In: UNSPECIFIED, ? - ?.

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Abstract

Gaussian process (GP) is well researched and used in machine learning field. Comparing with artificial neural network (ANN) and support vector regression (SVR), it provides additional covariance information for regression results. By exploiting this feature, an uncertainty based locational optimisation strategy combining with an entropy based data selection method for mobile sensor networks is presented in this paper. Centroidal Voronoi tessellation (CVT) is used as a locational optimisation framework and Informative Vector Machine (IVM) is applied for data selection. Simulations with different locational optimisation criteria are conducted and the results are given, which proved the effectiveness of presented strategy. © 2012 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings - 3rd International Conference on Emerging Security Technologies, EST 2012
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 16 Jan 2015 15:49
Last Modified: 23 Jan 2019 00:16
URI: http://repository.essex.ac.uk/id/eprint/9224

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